Today, the data flows for ML applications are more complex and varied than ever. To gain a competitive edge, businesses are increasingly including online models in their products or real-time processes, introducing a new range of operational concerns—and often, an explosion in the number of data sources and transformations used by their ML models.
In this whitepaper, learn:🔹 About different feature types and transformations, including batch, streaming, and real-time features
🔹 How to use a wide variety of batch, streaming, and real-time data to build powerful production ML models
🔹 The challenges of serving feature data and how to overcome them
🔹 The common misconceptions around ML, such as the idea that real-time ML means that the model only uses real-time data or that real-time transformations only happen on extremely fresh data